Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	106,436
2	South Dakota	94,214
3	Iowa	75,662
4	Wisconsin	73,256
5	Nebraska	69,638
6	Utah	64,334
7	Montana	61,043
8	Wyoming	60,608
9	Illinois	60,071
10	Idaho	59,808
11	Minnesota	59,167
12	Rhode Island	57,319
13	Kansas	56,846
14	Tennessee	55,596
15	Arkansas	54,446
16	Indiana	53,798
17	Mississippi	53,436
18	Alabama	53,099
19	Missouri	53,038
20	Louisiana	52,503
21	Nevada	52,048
22	Oklahoma	51,566
23	New Mexico	49,061
24	Florida	47,911
25	Arizona	47,829
26	Alaska	46,992
27	Texas	45,197
28	Georgia	44,262
29	Kentucky	43,613
30	South Carolina	43,324
31	Colorado	43,200
32	Michigan	41,091
33	New Jersey	39,723
34	Delaware	38,465
35	Ohio	38,228
36	North Carolina	36,076
37	Connecticut	35,390
38	Massachusetts	34,952
39	New York	34,896
40	Maryland	34,026
41	California	32,746
42	District of Columbia	31,404
43	Pennsylvania	30,667
44	Virginia	28,645
45	West Virginia	28,471
46	Puerto Rico	27,466
47	Washington	23,654
48	Oregon	18,800
49	New Hampshire	16,860
50	Hawaii	12,893
51	Maine	9,339
52	Vermont	7,439

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,258
2	Utah	1,092
3	South Dakota	1,086
4	Wyoming	1,021
5	Indiana	1,014
6	Arizona	965
7	Nebraska	964
8	Montana	931
9	New Mexico	918
10	Alaska	897
11	Idaho	881
12	Illinois	878
13	Minnesota	878
14	Kentucky	865
15	Iowa	854
16	Wisconsin	847
17	Louisiana	836
18	Connecticut	830
19	Colorado	826
20	Nevada	821
21	North Dakota	810
22	Arkansas	767
23	Alabama	736
24	Ohio	735
25	Michigan	734
26	Massachusetts	714
27	Pennsylvania	667
28	Tennessee	647
29	Mississippi	645
30	Delaware	616
31	Kansas	593
32	West Virginia	592
33	Missouri	572
34	Texas	556
35	Oklahoma	530
36	New Jersey	514
37	Georgia	490
38	California	478
39	New Hampshire	473
40	Florida	461
41	New York	447
42	North Carolina	425
43	Maryland	386
44	South Carolina	360
45	Washington	348
46	Oregon	303
47	District of Columbia	289
48	Virginia	260
49	Vermont	251
50	Maine	197
51	Puerto Rico	162
52	Hawaii	58

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,937
2	New York	1,765
3	Massachusetts	1,577
4	Connecticut	1,433
5	Louisiana	1,403
6	Rhode Island	1,321
7	Mississippi	1,303
8	North Dakota	1,289
9	South Dakota	1,167
10	Illinois	1,077
11	Michigan	1,004
12	District of Columbia	980
13	Arizona	938
14	Indiana	896
15	Georgia	883
16	Florida	878
17	South Carolina	867
18	Pennsylvania	859
19	Arkansas	846
20	Iowa	825
21	Delaware	799
22	New Mexico	797
23	Maryland	788
24	Texas	780
25	Alabama	770
26	Nevada	733
27	Missouri	696
28	Tennessee	692
29	Minnesota	681
30	Montana	676
31	Wisconsin	649
32	Nebraska	606
33	Colorado	579
34	Ohio	577
35	Kansas	576
36	Idaho	572
37	North Carolina	520
38	California	495
39	Kentucky	489
40	Virginia	485
41	Oklahoma	463
42	Wyoming	444
43	West Virginia	440
44	New Hampshire	400
45	Washington	399
46	Puerto Rico	361
47	Utah	286
48	Oregon	230
49	Hawaii	172
50	Alaska	169
51	Maine	163
52	Vermont	120

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	32
2	Wyoming	24
3	Nebraska	23
4	North Dakota	21
5	Iowa	18
6	Idaho	16
7	Illinois	16
8	New Mexico	16
9	Colorado	15
10	Indiana	15
11	Michigan	15
12	Missouri	15
13	Wisconsin	15
14	Pennsylvania	14
15	Alabama	13
16	Kansas	13
17	Montana	12
18	Minnesota	11
19	Nevada	11
20	West Virginia	10
21	Ohio	9
22	Arizona	8
23	Connecticut	8
24	Kentucky	8
25	Mississippi	8
26	New Jersey	8
27	Rhode Island	8
28	Tennessee	8
29	Louisiana	7
30	Oklahoma	7
31	Texas	7
32	Maine	6
33	Maryland	6
34	Massachusetts	6
35	Washington	6
36	Arkansas	5
37	District of Columbia	5
38	Georgia	5
39	North Carolina	5
40	Puerto Rico	5
41	South Carolina	5
42	Alaska	4
43	Florida	4
44	New Hampshire	4
45	Oregon	4
46	Utah	4
47	California	3
48	New York	3
49	Vermont	3
50	Virginia	3
51	Delaware	2
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	214,321	1	99
Norton	Kansas	206,491	2	99
Bon Homme	South Dakota	199,536	3	99
Buffalo	South Dakota	196,228	4	99
Lincoln	Arkansas	195,025	5	99
Davidson	Tennessee	69,859	495	84
Richland	South Carolina	50,245	1276	59
York	South Carolina	36,305	2065	34
Orange	California	26,434	2548	18
Pierce	Washington	21,135	2748	12

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,952	3	99
Hancock	Georgia	5,558	4	99
Turner	South Dakota	5,367	5	99
Richland	South Carolina	729	1374	56
Davidson	Tennessee	618	1606	48
Orange	California	499	1895	39
York	South Carolina	491	1918	38
Pierce	Washington	362	2249	28

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.